让照片换光不走形，关键在精准搬运光照特征。
Consistent Feature Transport for Image Relighting
- 提出光照特征一致性搬运机制，显式建模光源变化路径。
- 在复杂光照下保持身份与几何一致，显著优于现有方法。
- 适用于换光、风格迁移等编辑任务，代码已开源。
图像重光照在改变光照的同时需保留身份与几何等非光照内容。现有基于扩散模型的方法在复杂光照下常出现光照不稳定或内容不一致的问题，因其缺乏显式的图像间特征变换学习机制。本文将重光照重构为光照特征搬运问题，提出一致特征搬运（CFT）训练原则，通过轨迹级监督显式约束源图与目标图分布间的光照一致性特征传输。基于修正流构建的CFT联合建模噪声到图像生成与光照一致的源到目标特征搬运。该双搬运框架促使光照特异性变化被分离，同时保留内容对齐特征。为支持复杂光照场景，我们构建了一个大规模人像重光照数据集，包含多样化重光照效果。实验表明，相比现有最先进方法，CFT实现一致性能提升，并可泛化至风格迁移等其他编辑任务。代码已公开于https://github.com/Dixin-Lab/CFT。
原文摘要 · Abstract (English)
Image relighting modifies illumination while preserving non-lighting content such as identity and geometry. Existing diffusion-based methods often suffer from unstable illumination changes or inconsistent content preservation under complex lighting, as they lack an explicit mechanism to learn feature transformations between images. We reformulate relighting as an illumination feature transport problem and introduce Consistent Feature Transport (CFT), a training principle that explicitly enforces illumination-consistent transport between source and target image distributions. Built upon rectified flow, CFT jointly models noise-to-image generation and illumination-consistent source-to-target transport through trajectory-level supervision. This dual-transport formulation encourages isolation of illumination-specific variations while preserving content-aligned features. To support complex lighting scenarios, we construct a large-scale portrait relighting dataset with diverse relighting effects. Experiments show consistent improvements over existing state-of-the-art relighting approaches and demonstrate that CFT can generalize to other editing tasks, including style transfer. Code is available at https://github.com/Dixin-Lab/CFT.
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